From the retina to action: Understanding visual processing

Laurent Perrinet

Licence Sciences & Humanité, 3/4/2019

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement n°713750. Also, it has been carried out with the financial support of the Regional Council of Provence-Alpes-Côte d'Azur and with the financial support of the A*MIDEX (n°ANR-11-IDEX-0001-02). This work was granted access to the HPC resources of Aix-Marseille Université financed by the project Equip@Meso (ANR-10-EQPX-29-01) of the program "Investissements d’Avenir".

Churchland, P. S. & Sejnowski, T. J. (1992) The computational brain. Cambridge, MA: MIT Press.

From the retina to action: Levels of modeling

(see this viperlib page)

LP, Adams and Friston (2015) Biological Cybernetics

This schematic shows the dependencies among various quantities modelling exchanges of an agent with the environment. It shows the states of the environment and the system in terms of a probabilistic dependency graph, where connections denote directed (causal) dependencies. The quantities are described within the nodes of this graph -- with exemplar forms for their dependencies on other variables.
Hidden (external) and internal states of the agent are separated by action and sensory states. Both action and internal states -- encoding a conditional probability density function over hidden states -- minimise free energy. Note that hidden states in the real world and the form of their dynamics can be different from that assumed by the generative model; (this is why hidden states are in bold. )

LP, Adams and Friston (2015) Biological Cybernetics

Outline

From the retina to action: Levels of modeling

Sparse coding in Neurophysiology and neural processing

Modeling Spiking Neural Networks

A model of Sparse Deep Predictive Coding

LP (2015) "Sparse models" in Biologically Inspired Computer Vision

Ravello, LP, Escobar, Palacios (2019) Scientific Reports

Kremkow, LP, Monier, Alonso, Aertsen, Fregnac, Masson (2016) Push-pull receptive field organization and synaptic depression: Mechanisms for reliably encoding naturalistic stimuli in V1

LP (2015) "Sparse models" in Biologically Inspired Computer Vision

Olshausen and Field (1997) Sparse coding with an overcomplete basis set: A strategy employed by V1?

a seminal idea is proposed by Olshausen: * this may be formalized as an inference problem:

edges are different sources, which are known to be sparse: by mixing these sources one forms the image (transparency hypothesis)

the sparseness is characterized by the pdf of the sources coefficients

this inference problem is an inverse problem:
while this problem may be hard to solve, this may be approached using a (conjugate) gradient descent which has a nice implementation in terms of neural networks

I wish to point here to an essential feature compared to classical feed-forward networks: you can not do the inference in one single shot in general; you need a recurrent / recursive network (see arrow) and this is precisely a possible function for one of the most numerous type of synapses: short-ranges lateral interactions

Olshausen and Field (1997) Sparse coding with an overcomplete basis set: A strategy employed by V1?

a seminal idea is proposed by Olshausen: * this may be formalized as an inference problem:

edges are different sources, which are known to be sparse: by mixing these sources one forms the image (transparency hypothesis)

the sparseness is characterized by the pdf of the sources coefficients

this inference problem is an inverse problem:
while this problem may be hard to solve, this may be approached using a (conjugate) gradient descent which has a nice implementation in terms of neural networks

Olshausen and Field (1997) Sparse coding with an overcomplete basis set: A strategy employed by V1?

a seminal idea is proposed by Olshausen: * this may be formalized as an inference problem:

edges are different sources, which are known to be sparse: by mixing these sources one forms the image (transparency hypothesis)

the sparseness is characterized by the pdf of the sources coefficients

this inference problem is an inverse problem:
while this problem may be hard to solve, this may be approached using a (conjugate) gradient descent which has a nice implementation in terms of neural networks